Paper Citation Record · LEDGER

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.16481.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.16481 v3

Coverage vector

measured 48 of 48 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:54.573741Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 24e8b860-292c-4ee0-bec8-71f7d3dc00ee · outbound

This paper cites write newline.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling write newline

Reference 1

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Observation 977b0868-4149-4039-b139-c9226cad21a8 · outbound

This paper cites Doubly sparse variational Gaussian processes.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Doubly sparse variational Gaussian processes

Reference 2

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Observation 1aa78d69-d7ca-4f15-8f5b-bc70986a5f53 · outbound

This paper cites O., et al.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling O., et al

Reference 3

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Observation c475571e-5aa3-428d-820b-b1688707422e · outbound

This paper cites Sparse Gaussian Process Variational Autoencoders.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Sparse Gaussian Process Variational Autoencoders

Reference 4

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This paper cites T., and Katzfuss, M.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling T., and Katzfuss, M

Reference 5

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Observation 5b901be7-b3af-4b36-97d0-13774ef153c6 · outbound

This paper cites P., Dalca, A., Saglietti, L., Listgarten, J., and Fusi, N.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling P., Dalca, A., Saglietti, L., Listgarten, J., and Fusi, N

Reference 6

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Observation dcc0e175-b903-4e54-9dc9-17bb1ced1c1e · outbound

This paper cites E., Wilkinson, W.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling E., Wilkinson, W

Reference 7

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Observation 12c3eea7-bdcc-4c7f-ba23-6c680860510e · outbound

This paper cites Stochastic gradient Hamiltonian Monte Carlo.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Stochastic gradient Hamiltonian Monte Carlo

Reference 8

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Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Unresolved cited work

Reference 9

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Observation 8574df24-0a66-4f75-933d-f12034939eba · outbound

This paper cites Nearest-neighbor sparse Cholesky matrices in spatial statistics.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Nearest-neighbor sparse Cholesky matrices in spatial statistics

Reference 10

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Observation 576e876d-6287-4a00-891e-36beba3476e0 · outbound

This paper cites O., and Gelfand, A.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling O., and Gelfand, A

Reference 11

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Observation 6e8a3d3b-e582-4aa4-9fd6-efce72f11c35 · outbound

This paper cites O., and Gelfand, A.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling O., and Gelfand, A

Reference 12

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Observation a8497cb9-c1ab-4362-84ee-4bde727ca70d · outbound

This paper cites Gp-vae: Deep probabilistic time series imputation.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Gp-vae: Deep probabilistic time series imputation

Reference 13

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This paper cites R., Pleiss, G., Bindel, D., Weinberger, K.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling R., Pleiss, G., Bindel, D., Weinberger, K

Reference 14

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Observation e5706219-1549-4f28-a64d-18ccecd6ea12 · outbound

This paper cites Geostatistics for natural resources evaluation, volume 483.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Geostatistics for natural resources evaluation, volume 483

Reference 15

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Observation 0fa7205a-ea35-4d28-9e16-42be224f1ece · outbound

This paper cites Spatio-temporal variational Gaussian processes.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Spatio-temporal variational Gaussian processes

Reference 16

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Observation e9052f9d-db4a-4953-8375-6077886e45a0 · outbound

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Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Unresolved cited work

Reference 17

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Observation d92dcc53-2fc1-46fb-bd09-a077914799e5 · outbound

This paper cites G., Filippone, M., and Ghahramani, Z.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling G., Filippone, M., and Ghahramani, Z

Reference 18

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Observation 822f7f55-16d4-4418-bf80-8545d5bb22cd · outbound

This paper cites P., Glorot, X., Botvinick, M.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling P., Glorot, X., Botvinick, M

Reference 19

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Observation 132f65dd-2d7b-4c86-9953-488906226036 · outbound

This paper cites Scalable Gaussian process variational autoencoders.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Scalable Gaussian process variational autoencoders

Reference 20

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Observation bad8cfc3-fbf0-4027-bc1d-fbb0620ece2b · outbound

This paper cites Billion-scale similarity search with GPUs.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Billion-scale similarity search with GPUs

Reference 21

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This paper cites and Katzfuss, M.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling and Katzfuss, M

Reference 22

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Observation a90b8f16-9285-4d09-9311-2bb622e7c93e · outbound

This paper cites and Guinness, J.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling and Guinness, J

Reference 23

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Observation 90b5f988-1e05-4cc9-a3c6-7fee60a60ac0 · outbound

This paper cites and Mnih, A.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling and Mnih, A

Reference 24

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Observation 6d3c70ef-f733-4552-9735-0f3419f7a42f · outbound

This paper cites Auto-Encoding Variational Bayes.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Auto-Encoding Variational Bayes

Reference 25

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Observation db5a9d10-f430-454b-9389-3e6eb1253ce9 · outbound

This paper cites Deep Kalman Filters.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Deep Kalman Filters

Reference 26

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This paper cites Gradient-based learning applied to document recognition.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Gradient-based learning applied to document recognition

Reference 27

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This paper cites Handling Incomplete Heterogeneous Data using VAEs.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Handling Incomplete Heterogeneous Data using VAEs

Reference 28

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Observation 8b25b75f-0a72-4605-8eb3-410bd91e0c70 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Pytorch: An imperative style, high-performance deep learning library

Reference 29

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Observation 47c5138d-c941-4b08-9aa7-6da46e853239 · outbound

This paper cites The Gaussian process prior vae for interpretable latent dynamics from pixels.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling The Gaussian process prior vae for interpretable latent dynamics from pixels

Reference 30

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Observation c90e9085-cba1-407f-81e8-b2e92337e660 · outbound

This paper cites and Rasmussen, C.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling and Rasmussen, C

Reference 31

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Observation c8f25f9e-5f63-4134-8e06-1bad2ecec762 · outbound

This paper cites a \"a , K., Koskinen, M., and L \.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling a \"a , K., Koskinen, M., and L \

Reference 32

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Observation 7b686b9a-5dc1-449b-a7d4-75efe35644fb · outbound

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Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Unresolved cited work

Reference 33

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Observation 6c4aa11b-5f4a-49fc-b0db-2189b5024c7d · outbound

This paper cites Generating diverse high-fidelity images with vq-vae-2.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Generating diverse high-fidelity images with vq-vae-2

Reference 34

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This paper cites T., and Duvenaud, D.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling T., and Duvenaud, D

Reference 35

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Observation 1afce404-4024-4b58-94d9-0e05f2e2c059 · outbound

This paper cites and Solin, A.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling and Solin, A

Reference 36

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Reference 37

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Reference 38

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Observation 5dae4afa-23e9-48f7-80cd-1790b0809a04 · outbound

This paper cites K., Raiko, T., Maal e, L., S nderby, S.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling K., Raiko, T., Maal e, L., S nderby, S

Reference 39

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Observation 0c66e91b-f5a8-4f20-8e14-03edcfc51018 · outbound

This paper cites T., Klein, A., Falkner, S., and Hutter, F.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling T., Klein, A., Falkner, S., and Hutter, F

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:55.994144Z

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Observation 65f13da3-75a6-4e94-9194-b112dd0518de · outbound

This paper cites L., Chi, Z., and Welty, L.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling L., Chi, Z., and Welty, L

Reference 41

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Observation 0d7ca559-ea8b-4365-a6d6-d6bbbbe00f5a · outbound

This paper cites Variational learning of inducing variables in sparse Gaussian processes.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Variational learning of inducing variables in sparse Gaussian processes

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:55.799074Z

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Observation 132cfd0b-0ac0-4890-a446-53b354b3f163 · outbound

This paper cites V., and Filippone, M.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling V., and Filippone, M

Reference 43

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Observation 87a7acd0-de7e-4887-bf92-3fd1dd09277e · outbound

This paper cites Fully Bayesian autoencoders with latent sparse Gaussian processes.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Fully Bayesian autoencoders with latent sparse Gaussian processes

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:55.603925Z

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Observation 1c5259c3-8ad5-4b94-a359-2935bad11205 · outbound

This paper cites Sparse within sparse Gaussian processes using neighbor information.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Sparse within sparse Gaussian processes using neighbor information

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:55.454045Z

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Observation 49074b90-8599-494a-9c0c-5e47864bf6db · outbound

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Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Unresolved cited work

Reference 46

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Observation 83fad259-1cfe-4917-aa94-f04487562224 · outbound

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Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:05:55.074391Z

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Observation dc9ff54d-786b-44b4-80e3-fe4cdf9cead4 · outbound

This paper cites Markovian Gaussian process variational autoencoders.

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling Markovian Gaussian process variational autoencoders

Reference 48

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.